Time-series　reconstruction　for　cloud/shadow-covered　optical　satellite　images　has　great　significance　for　enhancing　the　data　availability　and　temporal　change　analysis.　In　this　study,　we　proposed　a　superpixel-based　prediction　transformation-fusion　(SPTF)　time-series　reconstruction　method　for　cloud/shadow-covered　optical　images.　Central　to　this　approach　is　the　incorporation　between　intrinsic　tendency　from　multi-temporal　optical　images　and　sequential　transformation　information　from　synthetic　aperture　radar　(SAR)　data,　through　autoencoder　networks　(AE).　First,　a　modified　superpixel　algorithm　was　applied　on　multi-temporal　optical　images　with　their　manually　delineated　cloud/shadow　masks　to　generate　superpixels.　Second,　multi-temporal　optical　images　and　SAR　data　were　overlaid　onto　superpixels　to　produce　superpixel-wise　time-series　curves　with　missing　values.　Third,　these　superpixel-wise　time　series　were　clustered　by　an　AE-LSTM　(long　short-term　memory)　unsupervised　method　into　multiple　clusters　(searching　similar　superpixels).　Four,　for　each　superpixel-wise　cluster,　a　prediction-transformation-based　reconstruction　model　was　established　to　restore　missing　values　in　optical　time　series.　Finally,　reconstructed　data　were　merged　with　cloud-free　regions　to　produce　cloud-free　time-series　images.　The　proposed　method　was　verified　on　two　datasets　of　multi-temporal　cloud/shadow-covered　Landsat　OLI　images　and　Sentinel-1A　SAR　data.　The　reconstruction　results,　showing　an　improvement　of　greater　than　20%　in　normalized　mean　square　error　compared　to　three　state-of-the-art　methods　(including　a　spatially　and　temporally　weighted　regression　method,　a　spectral-temporal　patch-based　method,　and　a　patch-based　contextualized　AE　method),　demonstrated　the　effectiveness　of　the　proposed　method　in　time-series　reconstruction　for　multi-temporal　optical　images.